Extending K-Anonymity to Privacy Preserving Data Mining Using Association Rule Hiding Algorithm

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Privacy Preserving Data Mining is a research area concerned with the privacy driven from personally identifiable information when considered for data mining. k-anonymity is one of the most classic models, which prevents joining attacks by generalizing or suppressing portions of the released micro data so that no individual can be uniquely distinguished from a group of size k. This paper focuses on how to extend k-anonymity to privacy preserving data mining using association rule hiding algorithm. Association rule hiding algorithm refers to the process of modifying the original database in such a way that certain sensitive association rules disappear without seriously affecting the data and the non sensitive rules.